After reading through this tutorial, you may keep this tab
open and refer back to the tutorial as you complete the rest of the
survey.
Introduction
Technical
objective
Learn the basics of Bayesian parameter estimation, a Bayesian data
analysis technique, and how it differs from frequentist parameter
estimation
Substantive research
question
How do emotion regulation strategies moderate the association between
sleep and negative emotions?
Bayesian parameter
estimation
In this tutorial, we will be applying the Bayesian lens to the
estimation of parameters in a multilevel linear model. In
Bayesian parameter estimation, we view parameter
estimates as probability distributions, as opposed to point values.
Emotion
regulation
In the following Bayesian data analysis models, we will examine two
emotion regulation strategies, cognitive reappraisal
and expressive suppression, and whether they moderate
the association between hours of sleep at night and
negative affect the following day.
Briefly, in cognitive reappraisal, one changes the
way in which they think about an emotionally-inducing event in order to
alter their emotional response to that event. For example, instead of
thinking about an exam as a difficult and consequential test of their
abilities, a student might choose to frame the exam as an opportunity to
show what they have learned. In expressive suppression,
one regulates their emotions by inhibiting their external displays of
emotion. For example, a student who feels nervous about an upcoming exam
may stop themselves from showing a worried look on their face and
telling their friends that they feel nervous. More information on these
two emotion regulation strategies can be found in Gross & John
(2003).
The present study:
Emotion regulation, sleep, and negative affect
In our analysis, we will be looking at the relationships among these
emotion regulation strategies, sleep, and negative affect, also known as
negative emotion. An individual’s emotion regulation and coping
strategies are thought to moderate the impact of stress on sleep quality
(Kahn et al., 2013). We might also be interested in whether emotion
regulation strategies moderate the impact of sleep on negative emotions
the following day. In other words, does a particular emotion
regulation strategy help us better manage our negative emotions after a
night of poor sleep?
The data we are using come from the AMIB study, a multiple timescale
study of college students (Ram et al., 2012). In particular, we will be
looking at college students’ daily self-reports of their sleep and
negative affect over the course of eight days, as well as their
dispositional cognitive reappraisal and expressive suppression scores.
The data can be downloaded from https://thechangelab.stanford.edu/collaborations/the-amib-data/.
We will run a regression analysis using these data to get some
preliminary insights into the associations among emotion regulation,
sleep, and negative affect.
Preliminaries
Load libraries and
data
library(brms)
library(loo)
library(psych)
library(tidyverse)
We read in the data from the online repository.
# set filepath for data file
filepath <-
"https://raw.githubusercontent.com/The-Change-Lab/collaborations/main/AMIB/AMIB_persons.csv"
# read in the .csv file using the url() function
AMIB_persons <- read.csv(file=url(filepath),header=TRUE)
# set filepath for data file
filepath <-
"https://raw.githubusercontent.com/The-Change-Lab/collaborations/main/AMIB/AMIB_daily.csv"
# read in the .csv file using the url() function
AMIB_daily <- read.csv(file=url(filepath),header=TRUE)
Data
manipulation
We merge the daily-level variables (participant ID,
day, hours of sleep, and
negative affect) and the person-level variables
(participant ID, cognitive reappraisal
score, expressive suppression score) into a single
dataset.
# subset to day-level variables of interest
bpe_daily <- AMIB_daily[,c("id", "day", "slphrs", "negaff")]
# subset to person-level variables of interest
bpe_persons <- AMIB_persons[,c("id", "erq_reap", "erq_supp")]
# merge day- and person-level data
bpe_data <- merge(bpe_daily, bpe_persons, by = "id")
We center the emotion regulation questionnaire scores to facilitate
the interpretation of model results.
# center the erq subscale score variables for interpretability
bpe_data$erq_reap_c <- scale(bpe_data$erq_reap, center=TRUE,scale=FALSE)[,1]
bpe_data$erq_supp_c <- scale(bpe_data$erq_supp, center=TRUE,scale=FALSE)[,1]
Initial plots
Before running our models, it can be helpful to examine the raw
data.
Correlations and
distributions
# calculate correlations
# dropping the id and non-centered erq columns
cor(bpe_data[ ,c(-1, -5, -6)], use = "complete.obs")
## day slphrs negaff erq_reap_c erq_supp_c
## day 1.000000000 -0.0480824055 -0.141990043 0.007004748 -0.0210526130
## slphrs -0.048082405 1.0000000000 -0.155243242 0.001261147 -0.0004183473
## negaff -0.141990043 -0.1552432419 1.000000000 -0.002317911 0.0246226777
## erq_reap_c 0.007004748 0.0012611469 -0.002317911 1.000000000 -0.1337777409
## erq_supp_c -0.021052613 -0.0004183473 0.024622678 -0.133777741 1.0000000000
# plot correlations
pairs.panels(bpe_data[ ,c(-1, -5, -6)])

We see relatively weak correlations overall, but note a small
negative correlation between hours of sleep and
negative affect (-0.16), as well as a small negative
correlation between cognitive reappraisal and
expressive suppression (-0.13). We also observe a small
negative correlation between day in study and
negative affect (-0.14).
The distributions of the variables can be seen on the diagonal. Of
particular interest, we note that the distribution of cognitive
reappraisal scores has a negative skew, while the distribution
of expressive suppression scores is relatively
symmetrical.
Linear regression:
Negative affect vs. hours of sleep
We will now plot a simple, non-Bayesian linear regression of
negative affect vs. hours of sleep to
get a better sense of the association between our outcome variable and
our main predictor variable.
ggplot(data=bpe_data, aes(x=slphrs, y=negaff)) +
geom_jitter(width = 0.35) +
geom_smooth(method=lm, lty=1, size=2) +
xlab("Hours of Sleep") + ylab("Negative Affect Level") +
theme_classic() +
theme(axis.title=element_text(size=12),
axis.text=element_text(size=12),
plot.title=element_text(size=14, hjust=.5)) +
theme(text = element_text(family = "Times New Roman")) +
ggtitle("Negative Affect vs. Sleep")

In general, more hours of sleep is associated with
lower negative affect. However, many of the sleep
durations correspond to a wide range of negative affect
values, suggesting differences in negative affect
across individuals.
The Bayesian
approach
Bayes’ theorem
Before running our Bayesian models, we will briefly recap Bayes’
theorem. Bayes’ theorem gives us a mathematical expression for the
probability of a hypothesis, H, conditional on (that is, given existing
knowledge of) a body of evidence, E (Joyce, 2003/2021). The expression
for Bayes’ theorem is
\[ P(H|E)= \frac{P(E|H)P(H)}{P(E)}.
\] The posterior, \(P(H|E)\),
refers to the probability of the hypothesis being true after having
observed the evidence. The likelihood, \(P(E|H)\), refers to the probability of
observing the evidence in the case where the hypothesis is already true.
The prior, \(P(H)\), refers to the
probability of the hypothesis being true without having observed any
evidence. Finally, the normalizing constant, \(P(E)\), refers to the probability of the
evidence occurring independent of any hypothesis.
Bayesian
interpretation
In general, Bayes’ theorem operates under the assumption that our
refined degree of belief in H can be expressed in terms of (i) our
prior, or existing, degree of belief in H and (ii) the contribution of
newly observed evidence.
Of interest to parameter estimation, Bayes’ theorem allows us to
calculate the probability distribution of a particular variable. In
Bayesian parameter estimation, we use Bayes’ theorem to estimate the
posterior distribution of a parameter value conditioned on our data,
that is, \(P(\theta|D)\). Compared to
frequentist parameter estimation, which calculates point estimates for
parameter values, Bayesian parameter estimation gives us a new
way to quantify how uncertain we are about our parameter estimates
through the use of probability distributions.
Cognitive reappraisal
model
We will first estimate a model using the cognitive
reappraisal score as a potential moderator for the relationship
between the hours of sleep one reports on a particular
night and their reported negative affect level the
following day.
Note that we are using the brms package (Bürkner,
2017) to fit our Bayesian models. This package allows the user to
specify prior distributions for parameters. However, with a relatively
simple linear model such as this one, we can instead allow brms to
automatically select uninformative priors for the model’s
parameters.
Model equation
\[ negaff_{it} = \beta_0 + \beta_1 day_{t}
+ \beta_2 sleephours_{it} + \beta_3 reappraisal_i + \beta_4
sleephours_{it} \times reappraisal_i + u_{it}\]
Run model
bpe.reap <- brm(negaff ~ 1 + day + slphrs + erq_reap_c + slphrs:erq_reap_c
+ (1 + slphrs + erq_reap_c|id),
data = bpe_data, family = gaussian(),
iter = 2000, chains = 4, cores = 4)
## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: negaff ~ 1 + day + slphrs + erq_reap_c + slphrs:erq_reap_c + (1 + slphrs + erq_reap_c | id)
## Data: bpe_data (Number of observations: 1421)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Group-Level Effects:
## ~id (Number of levels: 190)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## sd(Intercept) 0.97 0.12 0.74 1.23 1.00 1522
## sd(slphrs) 0.10 0.02 0.05 0.13 1.01 452
## sd(erq_reap_c) 0.09 0.07 0.00 0.26 1.00 745
## cor(Intercept,slphrs) -0.75 0.09 -0.88 -0.54 1.00 1944
## cor(Intercept,erq_reap_c) 0.03 0.47 -0.86 0.85 1.00 2095
## cor(slphrs,erq_reap_c) -0.14 0.51 -0.93 0.86 1.00 1707
## Tail_ESS
## sd(Intercept) 2350
## sd(slphrs) 671
## sd(erq_reap_c) 1087
## cor(Intercept,slphrs) 2207
## cor(Intercept,erq_reap_c) 2432
## cor(slphrs,erq_reap_c) 2210
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept 3.25 0.13 2.99 3.51 1.00 3878 2817
## day -0.07 0.01 -0.08 -0.05 1.00 7817 2844
## slphrs -0.08 0.02 -0.11 -0.05 1.00 4169 3344
## erq_reap_c -0.01 0.12 -0.23 0.22 1.00 3821 3195
## slphrs:erq_reap_c 0.00 0.01 -0.03 0.03 1.00 3941 3073
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 0.78 0.02 0.75 0.81 1.00 2981 2621
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
## Estimate Est.Error Q2.5 Q97.5
## R2 0.4451373 0.01807948 0.408227 0.4794034
Interpret
results
Check model
convergence
We see that all R-hat values are \(\leq\) 1.01, which indicates good
convergence.
Assess 95% credible
intervals
Since a Bayesian model does not give us p-values like a
traditional frequentist model would, we can instead use something called
a 95% credible interval to examine statistical
significance. We receive two values for each estimated parameter: the
interval’s lower bound (l-95% CI) and the interval’s upper bound (u-95%
CI). Given the data, the probability that the true parameter lies
between these values is 0.95.
One way to evaluate a 95% credible interval is to see whether the
interval contains 0. If 0 falls within the 95% credible interval, this
suggests that the parameter may not be significant. If 0 does not fall
within the 95% credible interval, we can be reasonably sure that the
parameter is significant.
For this model, the credible intervals of day and
hours of sleep do not contain 0, suggesting significant
associations between these variables and negative
affect. However, the credible intervals of cognitive
reappraisal and the interaction between hours of
sleep and cognitive reappraisal, the
predictors we are most interested in, do contain 0. Thus, we might doubt
the significance of these parameters.
View posterior
distributions of parameters



We can generate plots to visualize the posterior distributions for
our estimated parameters. Most parameters appear relatively normally
distributed, with a few positively- and negatively-skewed
exceptions.
Visualize model
We plot the model’s predictions for negative affect
vs. hours of sleep for “high” (\(\geq\) 0) and “low” (\(<\) 0) centered cognitive
reappraisal scores.
# save model predictions to dataset
bpe_data$pred_reap <- predict(bpe.reap, newdata=bpe_data, allow_new_levels=TRUE)
# discretize reappraisal variable for plotting purposes
bpe_data$erq_reap_hl <- ifelse(bpe_data$erq_reap_c >= 0, "High", "Low")
ggplot(data = bpe_data, aes(x = slphrs, y = pred_reap[,"Estimate"],
color = erq_reap_hl)) +
geom_jitter(size = .75, width = 0.35) +
geom_smooth(method = lm) +
xlab("Hours of Sleep") +
ylab("Negative Affect") +
theme_classic() +
theme(axis.title=element_text(size=12),
axis.text=element_text(size=12),
plot.title=element_text(size=14, hjust=.5)) +
theme(text = element_text(family = "Times New Roman")) +
ggtitle("Negative Affect vs. Sleep\nfor High and Low Reappraisal Individuals") +
labs(colour = "Cognitive Reappraisal Score")

Read plot
In this visualization, each point plots an individual’s reported
negative affect on a particular day against the number
of hours of sleep they report from the prior night.
Points are color coded to indicate whether the individual has a high
(coral) or low (turquoise) cognitive reappraisal
score.
The coral regression line represents the aggregate of individuals
with high cognitive reappraisal scores, while the
turquoise regression line represents the aggregate of individuals with
low cognitive reappraisal scores. The slope of each
line indicates the strength of the association between hours of
sleep at night and negative affect the
following day for that group. We note that the coral line has a more
negative slope than the turquoise line, indicating a stronger
association between hours of sleep and negative
affect for the high cognitive reappraisal
group.
Interpret plot
We observe that having a higher cognitive
reappraisal score may predict a larger difference in
negative affect for a fixed difference in hours
of sleep. In other words, it appears that those with higher
cognitive reappraisal scores report negative
affect that is more closely related to how many hours
of sleep they got the previous night. This may suggest that
cognitive reappraisal is most effective in decreasing
negative affect when an individual has gotten
sufficient hours of sleep.
Expressive suppression
model
Next, we will estimate a very similar model to examine the
expressive suppression score as a potential moderator,
in place of the cognitive reappraisal score.
Model equation
\[ negaff_{it} = \beta_0 + \beta_1 day_{t}
+ \beta_2 sleephours_{it} + \beta_3 suppression_i + \beta_4
sleephours_{it} \times suppression_i + u_{it}\]
Run model
bpe.supp <- brm(negaff ~ 1 + day + slphrs + erq_supp_c + slphrs:erq_supp_c
+ (1 + slphrs + erq_supp_c|id),
data = bpe_data, family = gaussian(),
iter = 2000, chains = 4, cores = 4)
## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: negaff ~ 1 + day + slphrs + erq_supp_c + slphrs:erq_supp_c + (1 + slphrs + erq_supp_c | id)
## Data: bpe_data (Number of observations: 1421)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Group-Level Effects:
## ~id (Number of levels: 190)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## sd(Intercept) 0.94 0.13 0.68 1.20 1.00 1085
## sd(slphrs) 0.09 0.02 0.05 0.13 1.01 468
## sd(erq_supp_c) 0.11 0.07 0.01 0.27 1.00 500
## cor(Intercept,slphrs) -0.73 0.11 -0.87 -0.46 1.00 1550
## cor(Intercept,erq_supp_c) 0.36 0.39 -0.54 0.93 1.00 1348
## cor(slphrs,erq_supp_c) -0.17 0.46 -0.90 0.77 1.00 1032
## Tail_ESS
## sd(Intercept) 1954
## sd(slphrs) 568
## sd(erq_supp_c) 925
## cor(Intercept,slphrs) 1555
## cor(Intercept,erq_supp_c) 1844
## cor(slphrs,erq_supp_c) 2053
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept 3.23 0.13 2.98 3.49 1.00 3936 2966
## day -0.07 0.01 -0.08 -0.05 1.00 10028 2715
## slphrs -0.08 0.02 -0.11 -0.05 1.00 4138 3393
## erq_supp_c 0.09 0.10 -0.12 0.28 1.00 2683 2776
## slphrs:erq_supp_c -0.01 0.01 -0.03 0.02 1.00 3047 2582
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 0.78 0.02 0.75 0.81 1.00 2543 3192
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
## Estimate Est.Error Q2.5 Q97.5
## R2 0.4438745 0.01804483 0.4068929 0.4780322
Interpret
results
Check model
convergence
We see that all R-hat values are \(\leq\) 1.01, which indicates good
convergence.
Assess 95% credible
intervals
We again will use each parameter’s 95% credible
interval to assess its significance. As a reminder, the
probability, given the data, that the true parameter falls within the
credible interval is 0.95. We can evaluate a 95% credible interval by
seeing whether the interval contains 0.
For this model, the credible intervals of day and
hours of sleep do not contain 0, suggesting significant
associations between these variables and negative
affect. However, the credible intervals of expressive
suppression and the interaction between hours of
sleep and expressive suppression, the
predictors we are most interested in, do contain 0. Thus, we might doubt
the significance of these parameters.
View posterior
distributions of parameters



We can generate plots to visualize the posterior distributions for
our estimated parameters. Most parameters appear relatively normally
distributed, with a few positively- and negatively-skewed
exceptions.
Visualize model
We plot the model’s predictions for negative affect
vs. hours of sleep for “high” (\(\geq\) 0) and “low” (\(<\) 0) centered expressive
suppression scores.
# save model predictions to dataset
bpe_data$pred_supp <- predict(bpe.supp, newdata=bpe_data, allow_new_levels=TRUE)
# discretize suppression variable for plotting purposes
bpe_data$erq_supp_hl <- ifelse(bpe_data$erq_supp_c >= 0, "High", "Low")
ggplot(data = bpe_data, aes(x = slphrs, y = pred_supp[,"Estimate"],
color = erq_supp_hl)) +
geom_jitter(size = .75, width = 0.35) +
geom_smooth(method = lm) +
xlab("Hours of Sleep") +
ylab("Negative Affect") +
theme_classic() +
theme(axis.title=element_text(size=12),
axis.text=element_text(size=12),
plot.title=element_text(size=14, hjust=.5)) +
theme(text = element_text(family = "Times New Roman")) +
ggtitle("Negative Affect vs. Sleep\nfor High and Low Suppression Individuals") +
labs(colour = "Expressive Suppression Score")

Read plot
In this visualization, each point plots an individual’s reported
negative affect on a particular day against the number
of hours of sleep they report from the prior night.
Points are color coded to indicate whether the individual has a high
(coral) or low (turquoise) expressive suppression
score.
The coral regression line represents the aggregate of individuals
with high expressive suppression scores, while the
turquoise regression line represents the aggregate of individuals with
low expressive suppression scores. The slope of each
line indicates the strength of the association between hours of
sleep at night and negative affect the
following day for that group. We note that the coral line has a more
negative slope than the turquoise line, indicating a stronger
association between sleep and negative
affect for the high expressive suppression
group.
Interpret plot
We observe that having a higher expressive
suppression score may predict a larger difference in
negative affect for a fixed difference in hours
of sleep. In other words, it appears that those with higher
expressive suppression scores report negative
affect that is more closely related to how many hours
of sleep they got the previous night. This may suggest that
expressive suppression is most effective in decreasing
negative affect when an individual has gotten
sufficient hours of sleep.
We observe that the moderating effects of cognitive
reappraisal and expressive suppression appear
similar, suggesting that a more general factor is moderating the
relationship between hours of sleep and
negative affect. We may want to investigate this
further in future work.
Model comparison
We will now use leave-one-out cross-validation (LOOCV) to compare the
relative fits of the cognitive reappraisal model and
the expressive suppression model. This type of model
comparison is typically done between two different kinds of models using
the same data. Our models have the same form but use slightly different
data (reappraisal predictor vs. suppression predictor). However, for
demonstrative purposes, we will stil compare them using LOOCV.
bpe.reap <- add_criterion(bpe.reap, c("loo"))
bpe.supp <- add_criterion(bpe.supp, c("loo"))
loo_compare(bpe.reap, bpe.supp)
## elpd_diff se_diff
## bpe.supp 0.0 0.0
## bpe.reap -1.4 1.6
We observe that the cognitive reappraisal model
appears to have a better fit than the expressive
suppression model. This indicates that the relationships among
variables in the cognitive reappraisal model are better
suited to a linear model than those in the expressive
suppression model. A LOOCV comparison would likely give us more
helpful information if we were comparing, for example, a linear version
of the cognitive reappraisal model and a nonlinear
version of the cognitive reappraisal model.
Beyond using formal quantitative techniques like LOOCV for model
comparison, it can be helpful to compare the plotted posterior
distributions of parameters across the two models. This can help us
understand the relative levels of uncertainty we have in those
models.
Conclusion
Shifting from a frequentist understanding of parameter estimation to
a Bayesian understanding can be challenging, and it is important to
consider the merits and drawbacks of both approaches. Bayesian parameter
estimation can seem unintuitive at first, but it provides us with new
tools to describe and analyze the uncertainty inherent in our data.
In social science research, being able to incorporate
subjectivity into our models can give us a new outlook on phenomena that
are often hard to pin down in a standard quantitative
framework.
Additional
resources
If you would like to learn more about the concepts mentioned in this
tutorial, here are a few external resources:
References
Bürkner, P.-C. (2017). brms: An R package for Bayesian multilevel
models using Stan. Journal of Statistical Software,
80(1). https://doi.org/10.18637/jss.v080.i01
Gross, J. J., & John, O. P. (2003). Individual differences in two
emotion regulation processes: Implications for affect, relationships,
and well-being. Journal of Personality and Social Psychology,
85(2), 348–362. https://doi.org/10.1037/0022-3514.85.2.348
Joyce, J. (2021). Bayes’ theorem (E. N. Zalta, Ed.). The Stanford
Encyclopedia of Philosophy (Fall 2021 Edition). https://plato.stanford.edu/archives/fall2021/entries/bayes-theorem/
(Original work published 2003)
Kahn, M., Sheppes, G., & Sadeh, A. (2013). Sleep and emotions:
Bidirectional links and underlying mechanisms. International Journal
of Psychophysiology, 89(2), 218–228. https://doi.org/10.1016/j.ijpsycho.2013.05.010
Ram, N., Conroy, D. E., Pincus, A. L., Hyde, A. L., & Molloy, L.
E. (2012). Tethering theory to method: Using measures of intraindividual
variability to operationalize individuals’ dynamic characteristics. In
G. Hancock & J. Harring (Eds.), Advances in longitudinal methods
in the social and behavioral sciences (pp. 81-110). New York:
Information Age.
You may now switch back to the Qualtrics survey tab. You can
leave this tab open and refer back to the tutorial as you complete the
rest of the survey.
---
title: "Modeling with uncertainty: A Bayesian parameter estimation tutorial"
output:
  html_document:
    toc: true
    toc_float: true
    toc_depth: 4
    code_download: true
    number_sections: true
    theme: flatly
    highlight: tango
  pdf_document: default
  word_document: default
editor_options:
  chunk_output_type: inline
---

<font size="4">

**After reading through this tutorial, you may keep this tab open and refer back to the tutorial as you complete the rest of the survey.**

# Introduction

## Technical objective
Learn the basics of Bayesian parameter estimation, a Bayesian data analysis technique, and how it differs from frequentist parameter estimation

## Substantive research question
How do emotion regulation strategies moderate the association between sleep and negative emotions?

## Bayesian parameter estimation
In this tutorial, we will be applying the Bayesian lens to the estimation of parameters in a multilevel linear model. In **Bayesian parameter estimation**, we view parameter estimates as probability distributions, as opposed to point values.  

## Emotion regulation
In the following Bayesian data analysis models, we will examine two emotion regulation strategies, **cognitive reappraisal** and **expressive suppression**, and whether they moderate the association between **hours of sleep** at night and **negative affect** the following day.

Briefly, in **cognitive reappraisal**, one changes the way in which they think about an emotionally-inducing event in order to alter their emotional response to that event. For example, instead of thinking about an exam as a difficult and consequential test of their abilities, a student might choose to frame the exam as an opportunity to show what they have learned. In **expressive suppression**, one regulates their emotions by inhibiting their external displays of emotion. For example, a student who feels nervous about an upcoming exam may stop themselves from showing a worried look on their face and telling their friends that they feel nervous. More information on these two emotion regulation strategies can be found in Gross & John (2003).

## The present study: Emotion regulation, sleep, and negative affect

In our analysis, we will be looking at the relationships among these emotion regulation strategies, sleep, and negative affect, also known as negative emotion. An individual's emotion regulation and coping strategies are thought to moderate the impact of stress on sleep quality (Kahn et al., 2013). We might also be interested in whether emotion regulation strategies moderate the impact of sleep on negative emotions the following day. In other words, **does a particular emotion regulation strategy help us better manage our negative emotions after a night of poor sleep?** 

The data we are using come from the AMIB study, a multiple timescale study of college students (Ram et al., 2012). In particular, we will be looking at college students' daily self-reports of their sleep and negative affect over the course of eight days, as well as their dispositional cognitive reappraisal and expressive suppression scores. The data can be downloaded from https://thechangelab.stanford.edu/collaborations/the-amib-data/. We will run a regression analysis using these data to get some preliminary insights into the associations among emotion regulation, sleep, and negative affect.

# Preliminaries

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE)
set.seed(1)
```

## Load libraries and data

```{r libraries}
library(brms)
library(loo)
library(psych)
library(tidyverse)
```

We read in the data from the online repository.

```{r person-level data}
# set filepath for data file
filepath <-
  "https://raw.githubusercontent.com/The-Change-Lab/collaborations/main/AMIB/AMIB_persons.csv"
# read in the .csv file using the url() function
AMIB_persons <- read.csv(file=url(filepath),header=TRUE)
```

```{r day-level data}
# set filepath for data file
filepath <-
  "https://raw.githubusercontent.com/The-Change-Lab/collaborations/main/AMIB/AMIB_daily.csv"
# read in the .csv file using the url() function
AMIB_daily <- read.csv(file=url(filepath),header=TRUE)
```

## Data manipulation

We merge the daily-level variables (**participant ID**, **day**, **hours of sleep**, and **negative affect**) and the person-level variables (**participant ID**, **cognitive reappraisal** score, **expressive suppression** score) into a single dataset.

```{r format data}
# subset to day-level variables of interest
bpe_daily <- AMIB_daily[,c("id", "day", "slphrs", "negaff")]
# subset to person-level variables of interest
bpe_persons <- AMIB_persons[,c("id", "erq_reap", "erq_supp")]
# merge day- and person-level data
bpe_data <- merge(bpe_daily, bpe_persons, by = "id")
```

We center the emotion regulation questionnaire scores to facilitate the interpretation of model results.

```{r center predictor}
# center the erq subscale score variables for interpretability
bpe_data$erq_reap_c <- scale(bpe_data$erq_reap, center=TRUE,scale=FALSE)[,1]
bpe_data$erq_supp_c <- scale(bpe_data$erq_supp, center=TRUE,scale=FALSE)[,1]
```

# Initial plots

Before running our models, it can be helpful to examine the raw data.

## Correlations and distributions

```{r plot correlations}
# calculate correlations
  # dropping the id and non-centered erq columns
cor(bpe_data[ ,c(-1, -5, -6)], use = "complete.obs")
# plot correlations
pairs.panels(bpe_data[ ,c(-1, -5, -6)])
```

We see relatively weak correlations overall, but note a small negative correlation between **hours of sleep** and **negative affect** (-0.16), as well as a small negative correlation between **cognitive reappraisal** and **expressive suppression** (-0.13). We also observe a small negative correlation between **day** in study and **negative affect** (-0.14).

The distributions of the variables can be seen on the diagonal. Of particular interest, we note that the distribution of **cognitive reappraisal** scores has a negative skew, while the distribution of **expressive suppression** scores is relatively symmetrical.

## Linear regression: Negative affect vs. hours of sleep

We will now plot a simple, non-Bayesian linear regression of **negative affect** vs. **hours of sleep** to get a better sense of the association between our outcome variable and our main predictor variable.

```{r negaff vs. slphrs}
ggplot(data=bpe_data, aes(x=slphrs, y=negaff)) +
  geom_jitter(width = 0.35) +
  geom_smooth(method=lm, lty=1, size=2) +
  xlab("Hours of Sleep") + ylab("Negative Affect Level") +
  theme_classic()  +
  theme(axis.title=element_text(size=12),
        axis.text=element_text(size=12),
        plot.title=element_text(size=14, hjust=.5)) +
  theme(text = element_text(family = "Times New Roman")) +
  ggtitle("Negative Affect vs. Sleep")
```

In general, more **hours of sleep** is associated with lower **negative affect**. However, many of the sleep durations correspond to a wide range of **negative affect** values, suggesting differences in **negative affect** across individuals.

# The Bayesian approach

## Bayes' theorem

Before running our Bayesian models, we will briefly recap Bayes' theorem. Bayes' theorem gives us a mathematical expression for the probability of a hypothesis, H, conditional on (that is, given existing knowledge of) a body of evidence, E (Joyce, 2003/2021). The expression for Bayes’ theorem is

$$ P(H|E)= \frac{P(E|H)P(H)}{P(E)}. $$
The posterior, $P(H|E)$, refers to the probability of the hypothesis being true after having observed the evidence. The likelihood, $P(E|H)$, refers to the probability of observing the evidence in the case where the hypothesis is already true. The prior, $P(H)$, refers to the probability of the hypothesis being true without having observed any evidence. Finally, the normalizing constant, $P(E)$, refers to the probability of the evidence occurring independent of any hypothesis.

## Bayesian interpretation

In general, Bayes’ theorem operates under the assumption that our refined degree of belief in H can be expressed in terms of (i) our prior, or existing, degree of belief in H and (ii) the contribution of newly observed evidence.

Of interest to parameter estimation, Bayes' theorem allows us to calculate the probability distribution of a particular variable. In Bayesian parameter estimation, we use Bayes' theorem to estimate the posterior distribution of a parameter value conditioned on our data, that is, $P(\theta|D)$. Compared to frequentist parameter estimation, which calculates point estimates for parameter values, **Bayesian parameter estimation gives us a new way to quantify how uncertain we are about our parameter estimates through the use of probability distributions.**

# Cognitive reappraisal model

We will first estimate a model using the **cognitive reappraisal** score as a potential moderator for the relationship between the **hours of sleep** one reports on a particular night and their reported **negative affect** level the following day.

Note that we are using the **brms** package (Bürkner, 2017) to fit our Bayesian models. This package allows the user to specify prior distributions for parameters. However, with a relatively simple linear model such as this one, we can instead allow brms to automatically select uninformative priors for the model's parameters.

## Model equation

$$ negaff_{it} = \beta_0 + \beta_1 day_{t} + \beta_2 sleephours_{it} + \beta_3 reappraisal_i + \beta_4 sleephours_{it} \times reappraisal_i + u_{it}$$

## Run model

```{r bpe model reap, results='hide'}
bpe.reap <- brm(negaff ~ 1 + day + slphrs + erq_reap_c + slphrs:erq_reap_c
                + (1 + slphrs + erq_reap_c|id),
                data = bpe_data, family = gaussian(),
                iter = 2000, chains = 4, cores = 4)
```

```{r bpe model reap sum}
summary(bpe.reap)
bayes_R2(bpe.reap)
```

## Interpret results

### Check model convergence

We see that all R-hat values are $\leq$ 1.01, which indicates good convergence.

### Assess 95% credible intervals

Since a Bayesian model does not give us *p*-values like a traditional frequentist model would, we can instead use something called a **95% credible interval** to examine statistical significance. We receive two values for each estimated parameter: the interval's lower bound (l-95% CI) and the interval's upper bound (u-95% CI). Given the data, the probability that the true parameter lies between these values is 0.95.

One way to evaluate a 95% credible interval is to see whether the interval contains 0. If 0 falls within the 95% credible interval, this suggests that the parameter may not be significant. If 0 does not fall within the 95% credible interval, we can be reasonably sure that the parameter is significant.

For this model, the credible intervals of  **day** and **hours of sleep** do not contain 0, suggesting significant associations between these variables and **negative affect**. However, the credible intervals of **cognitive reappraisal** and the interaction between **hours of sleep** and **cognitive reappraisal**, the predictors we are most interested in, do contain 0. Thus, we might doubt the significance of these parameters.

### View posterior distributions of parameters

```{r bpe model reap post}
plot(bpe.reap)
```

We can generate plots to visualize the posterior distributions for our estimated parameters. Most parameters appear relatively normally distributed, with a few positively- and negatively-skewed exceptions.

## Visualize model

We plot the model's predictions for **negative affect** vs. **hours of sleep** for "high" ($\geq$ 0) and "low" ($<$ 0) centered **cognitive reappraisal** scores.

```{r bpe model reap plot}
# save model predictions to dataset
bpe_data$pred_reap <- predict(bpe.reap, newdata=bpe_data, allow_new_levels=TRUE)
# discretize reappraisal variable for plotting purposes
bpe_data$erq_reap_hl <- ifelse(bpe_data$erq_reap_c >= 0, "High", "Low")

ggplot(data = bpe_data, aes(x = slphrs, y = pred_reap[,"Estimate"],
                                     color = erq_reap_hl)) +
  geom_jitter(size = .75, width = 0.35) +
  geom_smooth(method = lm) +
  xlab("Hours of Sleep") +
  ylab("Negative Affect") +
  theme_classic()  +
  theme(axis.title=element_text(size=12),
        axis.text=element_text(size=12),
        plot.title=element_text(size=14, hjust=.5)) +
  theme(text = element_text(family = "Times New Roman")) +
  ggtitle("Negative Affect vs. Sleep\nfor High and Low Reappraisal Individuals") +
  labs(colour = "Cognitive Reappraisal Score")
```

### Read plot

In this visualization, each point plots an individual's reported **negative affect** on a particular day against the number of **hours of sleep** they report from the prior night. Points are color coded to indicate whether the individual has a high (coral) or low (turquoise) **cognitive reappraisal** score.

The coral regression line represents the aggregate of individuals with high **cognitive reappraisal** scores, while the turquoise regression line represents the aggregate of individuals with low **cognitive reappraisal** scores. The slope of each line indicates the strength of the association between **hours of sleep** at night and **negative affect** the following day for that group. We note that the coral line has a more negative slope than the turquoise line, indicating a stronger association between **hours of sleep** and **negative affect** for the high **cognitive reappraisal** group.

### Interpret plot

We observe that having a higher **cognitive reappraisal** score may predict a larger difference in **negative affect** for a fixed difference in **hours of sleep**. In other words, it appears that those with higher **cognitive reappraisal** scores report **negative affect** that is more closely related to how many **hours of sleep** they got the previous night. This may suggest that **cognitive reappraisal** is most effective in decreasing **negative affect** when an individual has gotten sufficient **hours of sleep**.

# Expressive suppression model

Next, we will estimate a very similar model to examine the **expressive suppression** score as a potential moderator, in place of the **cognitive reappraisal** score.

## Model equation

$$ negaff_{it} = \beta_0 + \beta_1 day_{t} + \beta_2 sleephours_{it} + \beta_3 suppression_i + \beta_4 sleephours_{it} \times suppression_i + u_{it}$$

## Run model

```{r bpe model supp, results='hide'}
bpe.supp <- brm(negaff ~ 1 + day + slphrs + erq_supp_c + slphrs:erq_supp_c
                + (1 + slphrs + erq_supp_c|id),
                data = bpe_data, family = gaussian(),
                iter = 2000, chains = 4, cores = 4)
```

```{r bpe model supp sum}
summary(bpe.supp)
bayes_R2(bpe.supp)
```

## Interpret results

### Check model convergence

We see that all R-hat values are $\leq$ 1.01, which indicates good convergence.

### Assess 95% credible intervals

We again will use each parameter's **95% credible interval** to assess its significance. As a reminder, the probability, given the data, that the true parameter falls within the credible interval is 0.95. We can evaluate a 95% credible interval by seeing whether the interval contains 0.

For this model, the credible intervals of  **day** and **hours of sleep** do not contain 0, suggesting significant associations between these variables and **negative affect**. However, the credible intervals of **expressive suppression** and the interaction between **hours of sleep** and **expressive suppression**, the predictors we are most interested in, do contain 0. Thus, we might doubt the significance of these parameters.

### View posterior distributions of parameters

```{r bpe model supp post}
plot(bpe.supp)
```

We can generate plots to visualize the posterior distributions for our estimated parameters. Most parameters appear relatively normally distributed, with a few positively- and negatively-skewed exceptions.

## Visualize model

We plot the model's predictions for **negative affect** vs. **hours of sleep** for "high" ($\geq$ 0) and "low" ($<$ 0) centered **expressive suppression** scores.

```{r bpe model supp plot}
# save model predictions to dataset
bpe_data$pred_supp <- predict(bpe.supp, newdata=bpe_data, allow_new_levels=TRUE)
# discretize suppression variable for plotting purposes
bpe_data$erq_supp_hl <- ifelse(bpe_data$erq_supp_c >= 0, "High", "Low")

ggplot(data = bpe_data, aes(x = slphrs, y = pred_supp[,"Estimate"],
                                     color = erq_supp_hl)) +
  geom_jitter(size = .75, width = 0.35) +
  geom_smooth(method = lm) +
  xlab("Hours of Sleep") +
  ylab("Negative Affect") +
  theme_classic()  +
  theme(axis.title=element_text(size=12),
        axis.text=element_text(size=12),
        plot.title=element_text(size=14, hjust=.5)) +
  theme(text = element_text(family = "Times New Roman")) +
  ggtitle("Negative Affect vs. Sleep\nfor High and Low Suppression Individuals") +
  labs(colour = "Expressive Suppression Score")
```

### Read plot

In this visualization, each point plots an individual's reported **negative affect** on a particular day against the number of **hours of sleep** they report from the prior night. Points are color coded to indicate whether the individual has a high (coral) or low (turquoise) **expressive suppression** score.

The coral regression line represents the aggregate of individuals with high **expressive suppression** scores, while the turquoise regression line represents the aggregate of individuals with low **expressive suppression** scores. The slope of each line indicates the strength of the association between **hours of sleep** at night and **negative affect** the following day for that group. We note that the coral line has a more negative slope than the turquoise line, indicating a stronger association between **sleep** and **negative affect** for the high **expressive suppression** group.

### Interpret plot

We observe that having a higher **expressive suppression** score may predict a larger difference in **negative affect** for a fixed difference in **hours of sleep**. In other words, it appears that those with higher **expressive suppression** scores report **negative affect** that is more closely related to how many **hours of sleep** they got the previous night. This may suggest that **expressive suppression** is most effective in decreasing **negative affect** when an individual has gotten sufficient **hours of sleep**.

We observe that the moderating effects of **cognitive reappraisal** and **expressive suppression** appear similar, suggesting that a more general factor is moderating the relationship between **hours of sleep** and **negative affect**. We may want to investigate this further in future work.

# Model comparison

We will now use leave-one-out cross-validation (LOOCV) to compare the relative fits of the **cognitive reappraisal** model and the **expressive suppression** model. This type of model comparison is typically done between two different kinds of models using the same data. Our models have the same form but use slightly different data (reappraisal predictor vs. suppression predictor). However, for demonstrative purposes, we will stil compare them using LOOCV.

```{r loocv}
bpe.reap <- add_criterion(bpe.reap, c("loo"))
bpe.supp <- add_criterion(bpe.supp, c("loo"))
loo_compare(bpe.reap, bpe.supp)
```

We observe that the **cognitive reappraisal** model appears to have a better fit than the **expressive suppression** model. This indicates that the relationships among variables in the **cognitive reappraisal** model are better suited to a linear model than those in the **expressive suppression** model. A LOOCV comparison would likely give us more helpful information if we were comparing, for example, a linear version of the **cognitive reappraisal** model and a nonlinear version of the **cognitive reappraisal** model.  

Beyond using formal quantitative techniques like LOOCV for model comparison, it can be helpful to compare the plotted posterior distributions of parameters across the two models. This can help us understand the relative levels of uncertainty we have in those models.  

# Conclusion

Shifting from a frequentist understanding of parameter estimation to a Bayesian understanding can be challenging, and it is important to consider the merits and drawbacks of both approaches. Bayesian parameter estimation can seem unintuitive at first, but it provides us with new tools to describe and analyze the uncertainty inherent in our data. **In social science research, being able to incorporate subjectivity into our models can give us a new outlook on phenomena that are often hard to pin down in a standard quantitative framework.**

## Additional resources

If you would like to learn more about the concepts mentioned in this tutorial, here are a few external resources:

* The mathematics behind Bayesian parameter estimation: https://ccrma.stanford.edu/~jos/bayes/Bayesian_Parameter_Estimation.html
* Advanced topics on Bayesian parameter estimation: http://www.ece.virginia.edu/~ffh8x/docs/teaching/esl/2020-04/farnoud-slgm-chap03.pdf
* The brms package: https://paul-buerkner.github.io/brms/
* Confidence and credible intervals: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6630113/

# References

Bürkner, P.-C. (2017). brms: An R package for Bayesian multilevel models using Stan. *Journal of Statistical Software*, *80*(1). https://doi.org/10.18637/jss.v080.i01

Gross, J. J., & John, O. P. (2003). Individual differences in two emotion regulation processes: Implications for affect, relationships, and well-being. *Journal of Personality and Social Psychology*, *85*(2), 348–362. https://doi.org/10.1037/0022-3514.85.2.348

Joyce, J. (2021). Bayes’ theorem (E. N. Zalta, Ed.). *The Stanford Encyclopedia of Philosophy* (Fall 2021 Edition). https://plato.stanford.edu/archives/fall2021/entries/bayes-theorem/ (Original work published 2003)

Kahn, M., Sheppes, G., & Sadeh, A. (2013). Sleep and emotions: Bidirectional links and underlying mechanisms. *International Journal of Psychophysiology*, *89*(2), 218–228. https://doi.org/10.1016/j.ijpsycho.2013.05.010

Ram, N., Conroy, D. E., Pincus, A. L., Hyde, A. L., & Molloy, L. E. (2012). Tethering theory to method: Using measures of intraindividual variability to operationalize individuals’ dynamic characteristics. In G. Hancock & J. Harring (Eds.), *Advances in longitudinal methods in the social and behavioral sciences* (pp. 81-110). New York: Information Age.

**You may now switch back to the Qualtrics survey tab. You can leave this tab open and refer back to the tutorial as you complete the rest of the survey.**

</font>